Benefits Of Principal Component Analysis Research Articles

PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images.

Novel coronavirus 2019 has created a pandemic and was first reported in December 2019. It has had very adverse consequences on people's daily life, healthcare, and the world's economy as well. According to the World Health Organization's most recent statistics, COVID-19 has become a worldwide pandemic, and the number of infected persons and fatalities growing at an alarming rate. It is highly required to have an effective system to early detect the COVID-19 patients to curb the further spreading of the virus from the affected person. Therefore, to early identify positive cases in patients and to support radiologists in the automatic diagnosis of COVID-19 from X-ray images, a novel method PCA-IELM is proposed based on principal component analysis (PCA) and incremental extreme learning machine. The suggested method's key addition is that it considers the benefits of PCA and the incremental extreme learning machine. Further, our strategy PCA-IELM reduces the input dimension by extracting the most important information from an image. Consequently, the technique can effectively increase the COVID-19 patient prediction performance. In addition to these, PCA-IELM has a faster training speed than a multi-layer neural network. The proposed approach was tested on a COVID-19 patient's chest X-ray image dataset. The experimental results indicate that the proposed approach PCA-IELM outperforms PCA-SVM and PCA-ELM in terms of accuracy (98.11%), precision (96.11%), recall (97.50%), F1-score (98.50%), etc., and training speed.

Assessing Image Classification Accuracy with Principal Component Analysis Algorithm Case Study: Odeda LGA of Ogun State, Southwest Nigeria

The aim of this study is to assess image classification accuracy using the instrumentality of Principal Component Analysis (PCA). It is focused on evaluating the accruable benefits of Principal Component Analysis as part of an image preprocessing procedure for image classification. Land use land cover (LULC) and accuracy assessment datasets were obtained with remote sensing and geographic information system’s software. The principal component analysis was statistically used to assess the level of correlation amongst bands in Landsat 8. The image classification was premised on the Maximum Likelihood classifier for land use land cover analysis. To ascertain the accuracy of the classified images, the Producer’s accuracy, User’s accuracy and Kappa coefficient derivatives of accuracy assessment was calculated. The results revealed that the first three PCs of the raw Landsat data accounted for 99.37 % variance of the original Landsat data, while the last three PCs represented only 0.63% of the original data. The results of land use land cover based on raw bands composite were Forest (41%), Shrubs (33%) and Built-up (26%) respectively. On the other hand, land use land cover based on Principal Component Analysis showed Forest (39%), Shrubs (39%) and Built-up (22%) respectively. Comparing the results of Kappa coefficients of both LULC of raw bands’ composite was 0.88 while that of PCA was 0.91. Conclusively, there is a significant level of difference in the classification outputs of PCA derived classification and that of raw Landsat bands’ composite.

A hybrid feature fusion strategy for early fusion and majority voting for late fusion towards melanocytic skin lesion detection

AbstractA computer‐aided‐diagnostic system for diagnosing melanoma often uses distinct kinds of features for characterizing the lesions. Extracting distinct features from melanocytic images represents the various characteristics of pigmented lesions. Concatenating such features distinguishes extracted feature's information effectively while eliminating the redundant information amid them, which aids in discriminating the cancerous and noncancerous lesions. This article proposes a framework comprising segmentation, feature extraction, feature fusion, and classification to differentiate benign lesions from melanoma. The proposed framework is four‐fold: beginning with the extraction of ROI from an image, the SLICACO method is used for segmentation. Thereafter, ABCD rule‐based global and local features are extracted for effective melanoma detection. Further, we develop a new hybrid feature fusion strategy, PCAFA, leveraging the benefits of principal component analysis and factor analysis. The method performs early fusion by combining all extracted features within an individual feature vector and fed to a learning model for the prediction. While late fusion is performed using majority voting by combining the outputs of machine learning models. After that, gradient tree boosting, support vector machine, and decision tree models are trained to utilize distinct features of skin lesions for their classification as benign or malignant. The effectiveness of the designed framework is validated on the ISIC2017 benchmark skin lesion dataset based on specificity, sensitivity, and accuracy. The generalizability of the designed framework is gauged by conducting a fair comparison with conventional methods. Evaluated results reveal the potential of the proposed fused feature‐set in discriminating malignant and nonmalignant lesions with an accuracy of 96.8%.

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra.

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing "concentration" or "weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

Characterising deterioration patterns in fresh-cut fruit using principal component analysis. II: Effects of ripeness stage, seasonality, processing and packaging

Previous work has demonstrated the benefits of principal component analysis (PCA) in characterising and tracking processes of deterioration in fresh-cut fruit, and illustrating differences due to growing region and cultivar. In this study, use of PCA showed significant effects of season, stage of ripeness (p<0.05), cut size, packaging materials, storage temperature and time (p<0.01) for a range of fresh-cut fruit. PCA plots were effective in characterising patterns of deterioration and in tracking differences in quality, in terms of the rates and extent of change. Some of the effects studied were large and may form the basis for optimising raw materials, processing and packaging of several fresh-cut fruit. Differences in shelf-life of up to three days could be attributed to seasonal variation. Reduced process severity increased shelf-life by up to four days, while optimal packaging increased shelf-life by about three days.

&lt;b&gt;New hybrid multivariate analysis approach to optimize multiple response surfaces considering correlations in both inputs and outputs

Quality control in industrial and service systems requires the correct setting of input factors by which the outputs result at minimum cost with desirable characteristics. There are often more than one input and output in such systems. Response surface methodology in its multiple variable forms is one of the most applied methods to estimate and improve the quality characteristics of products with respect to control factors. When there is some degree of correlation among the variables, the existing method might lead into misleading improvement results. Current paper presents a new approach which takes the benefits of principal component analysis and multivariate regression to cope with the mentioned difficulties. Global criterion method of multiobjective optimization has been also used to reach a compromise solution which improves all response variables simultaneously. At the end, the proposed approach is described analytically by a numerical example.